Facebook's Wav2Vec2 · The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. Note: This model does not have a tokenizer as it was pretrained on audio alone.

The base model pretrained and fine-tuned on 960 hours of Librispeech on 16kHz sampled speech audio.

Wav2Vec2.0-base is a model, which pre-trained to learn speech representations on unlabeled data as described in wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations paper and fine-tuned for speech recognition task ...

Wav2vec 2.0 model (“base” architecture), pre-trained on 960 hours of unlabeled audio from LibriSpeech dataset [Panayotov et al., 2015] (the combination of “train-clean-100”, “train-clean-360”, and “train-other-500”), not fine-tuned. Originally published by the authors of wav2vec 2.0 [Baevski et al., 2020] under MIT License and redistributed with the same license. [License, Source] Please refer to torchaudio.pipelines.Wav2Vec2Bundle ...

The Wav2vec2 Base 960h model is a powerful tool for speech recognition. What makes it unique is its ability to learn from speech audio alone and fine-tune on transcribed speech, achieving state-of-the-art results with limited labeled data.

wav2vec2-base-960h is a pre-trained speech recognition model developed by Facebook. It is based on the Wav2Vec2 architecture and was trained on 960 hours of LibriSpeech data.

wav2vec2 · pretraining · speech · arxiv: 2404.01657 · License: apache-2.0 · Model card Files Files and versions · xet Community · 2 · Train · Deploy · Use this model · This is a Japanese wav2vec 2.0 Base model trained by rinna Co., Ltd.

Wav2vec 2.0 model (“base” architecture), pre-trained on 960 hours of unlabeled audio from LibriSpeech dataset [Panayotov et al., 2015] (the combination of “train-clean-100”, “train-clean-360”, and “train-other-500”), not fine-tuned. Originally published by the authors of wav2vec 2.0 [Baevski et al., 2020] under MIT License and redistributed with the same license. [License, Source] Please refer to torchaudio.pipelines.Wav2Vec2Bundle ...

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Indicates the valid length of each feature in the batch, computed based on the given lengths argument. Shape: (batch, ). ... torchaudio.models.wav2vec2_base(num_out: int) → torchaudio.models.wav2vec2.model.Wav2Vec2Model[source]¶

Base class for models that have been trained with the Wav2Vec2 loss objective.

We will use torchaudio.pipelines.WAV2VEC2_ASR_BASE_960H here.

Audio transcription model for converting English speech audio to English text

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$ fairseq-hydra-train \ task.data=/path/to/data \ --config-dir /path/to/fairseq-py/examples/wav2vec/config/pretraining \ --config-name wav2vec2_conformer_base_librispeech \ --attn-type espnet --pos-enc-type ${POS_ENC_TYPE}

October 10, 2024 - From this and the previous tables, we can safely conclude that models “ClTRUS”, “large” and “xlsr-53” all surpass the performance of the base wav2vec2 model.

We use wav2vec2 architecture for the pre-trained model.

It was introduced in the paper Transfer Learning of Transformer-Based Speech Recognition Models from Czech to Slovak accepted for the TSD2023 conference. This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model for speech recognition, a tokenizer should be created, and the model should be fine-tuned on labeled data. The model was initialized from the Czech pre-trained model fav-kky/wav2vec2-base-cs-80k-ClTRUS.

This is a Japanese wav2vec2.0 Base model pre-trained using 5313 hours of audio extracted from large-scale Japanese TV broadcast audio data by voice activity detection.

October 22, 2020 - We show for the first time that ... wav2vec 2.0 masks the speech input in the latent space and solves a contrastive task defined over a quantization of the latent representations which are jointly learned....